Invariant pattern recognition with higher-order neural networks

Invariant pattern recognition with higher-order neural networks

Translation, rotation (in plane) and scale invariant pattern recognition is a high-order recognition problem encountered frequently in real-world applications. But neither traditional image process/pattern recognition algorithms nor artificial neural networks have yet provided satisfactory solution...

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Bibliographic Details
Main Author: He, Zhengquan.
Other Authors: Mohammed Yakoob Siyal
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:http://hdl.handle.net/10356/4359
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Institution: Nanyang Technological University
Description
Summary:Translation, rotation (in plane) and scale invariant pattern recognition is a high-order recognition problem encountered frequently in real-world applications. But neither traditional image process/pattern recognition algorithms nor artificial neural networks have yet provided satisfactory solutions for this problem after years of study. Recent research has shown that a higher order neural networks (HONNs) of order three with built-in invariances can effectively achieve invariant pattern recognition.

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